Exploring Representation-Learning Approaches to Domain Adaptation

@inproceedings{Huang2010ExploringRA,
  title={Exploring Representation-Learning Approaches to Domain Adaptation},
  author={Fei Huang and Alexander Yates},
  year={2010}
}
Most supervised language processing systems show a significant drop-off in performance when they are tested on text that comes from a domain significantly different from the domain of the training data. Sequence labeling systems like partof-speech taggers are typically trained on newswire text, and in tests their error rate on, for example, biomedical data can triple, or worse. We investigate techniques for building open-domain sequence labeling systems that approach the ideal of a system whose… CONTINUE READING
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